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TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes

By Mutian Xu*, Pei Chen*, Haolin Liu, and Xiaoguang Han

Introduction

This repository is built for:

TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes (ECCV2022 Oral) [arXiv]

If you find our work useful in your research, please consider citing:

@inproceedings{xu2022toscene,
  title={TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes},
  author={Xu, Mutian and Chen, Pei and Liu, Haolin and Han, Xiaoguang},
  booktitle={ECCV},
  year={2022}
}

Dataset

TO-Scene contains 20,740 scenes with three different variants which are TO_Vanilla, TO_Crowd, TO_ScanNet.

1. Full TO-Scene data:

  • You can download our dataset with the corresponding variants from Google Drive:
Format TO_Vanilla TO_Crowd TO_ScanNet
ply (point cloud) Download (4.3GB) Download (2.1GB) Download (4.3GB)
npz (xyz, color, semantic_label, instance_label, bbox) Download (6.2GB) Download (2.8GB) Download (6.5GB)
  • Alternatively, for mainland China users, we also provide Baiduyun link:
Format TO_Vanilla TO_Crowd TO_ScanNet
ply (point cloud) Download (4.3GB) Download (2.1GB) Download (4.3GB)
npz (xyz, color, semantic_label, instance_label, bbox) Download (6.2GB) Download (2.8GB) Download (6.5GB)
  • Additionally, you can download our TO-Real data we mentioned in the paper,

2. Meta data:

You may also need to download meta_data at Google Drive or Baiduyun, including train/val/test split in format of .txt and pre-calculated mean_size_arr (mean size of objects of different classes).

3. Build your own dataset:

We have released the original CAD placement file (json file, describing the position, scale, and orientation of the small objects). You can run your own rendering and reconstruction based on this original annotation file, with parameters (e.g. point density) in your control.

Benchmark on TO-Scene

We have provided the code implementations for running 3D semantic segmentation and 3D object detection on our dataset, with the corresponding instructions.

Note that TO-Scene dataset contains 60,174 tabletop object instances from 52 common classes. For reference, we show the classes of these small tabletop objects below, which can be downloaded here as well.

Big furniture

Class Semantic Class Semantic Class Semantic
3 cabinet 9 window 24 refrigerator
4 bed 10 bookshelf 28 showercurtain
5 chair 11 picture 33 toilet
6 sofa 12 counter 34 sink
7 table 14 desk 36 bathtub
8 door 16 curtain 39 garbagebin

Small tabletop objects

Class Semantic Class Semantic Class Semantic Class Semantic
41 bag 54 laptop 67 chessboard 80 mirror
42 bottle 55 microphone 68 coffee_machine 81 notebook
43 bowl 56 microwave 69 comb 82 pencil
44 camera 57 mug 70 cutting_board 83 plant
45 can 58 printer 71 dishes 84 plate
46 cap 59 remote_control 72 doll 85 radio
47 clock 60 phone 73 eraser 86 ruler
48 keyboard 61 alarm 74 eye_glasses 87 saucepan
49 display 62 book 75 file_box 88 spoon
50 earphone 63 cake 76 fork 89 tea_pot
51 jar 64 calculator 77 fruit 90 toaster
52 knife 65 candle 78 globe 92 vase
53 lamp 66 charger 79 hat 93 vegetables

Contact

If you have any questions, please contact Mutian Xu ([email protected]) or Pei Chen ([email protected]).

News

The dataset challenge will be released soon, via a complete website!